





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def      a__			(       lowercase__	,     lowercase__=0.999	,     lowercase__="cosine"	,     ):






   '''simple docstring'''






   if alpha_transform_type == "cosine":

      def alpha_bar_fn(lowercase__	):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(lowercase__	):
         return math.exp(t * -12.0	)

   else:
      raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'	)

   UpperCAmelCase_           =[]
   for i in range(lowercase__	):
      UpperCAmelCase_           =i / num_diffusion_timesteps
      UpperCAmelCase_           =(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(lowercase__	) / alpha_bar_fn(lowercase__	)	,     lowercase__	)	)
   return torch.tensor(lowercase__	,     dtype=torch.floataa	)





class   A							(						__lowercase		,   __lowercase    ):
      _snake_case				  =[e.name for e in KarrasDiffusionSchedulers]
      _snake_case				  =2



      @register_to_config
      def __init__(						self:    Dict		,	_lowerCAmelCase:    int = 1000		,	_lowerCAmelCase:    float = 0.0_00_85		,	_lowerCAmelCase:    float = 0.0_12		,	_lowerCAmelCase:    str = "linear"		,	_lowerCAmelCase:    Optional[Union[np.ndarray, List[float]]] = None		,	_lowerCAmelCase:    str = "epsilon"		,	_lowerCAmelCase:    Optional[bool] = False		,	_lowerCAmelCase:    Optional[bool] = False		,	_lowerCAmelCase:    float = 1.0		,	_lowerCAmelCase:    str = "linspace"		,	_lowerCAmelCase:    int = 0		,	)    ->     Tuple:
         '''simple docstring'''

         if trained_betas is not None:
            UpperCAmelCase_           =torch.tensor(_lowerCAmelCase		,	dtype=torch.floataa							)
         elif beta_schedule == "linear":
            UpperCAmelCase_           =torch.linspace(_lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase		,	dtype=torch.floataa							)
         elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            UpperCAmelCase_           =(
                torch.linspace(beta_start**0.5		,	beta_end**0.5		,	_lowerCAmelCase		,	dtype=torch.floataa							) ** 2
            )
         elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            UpperCAmelCase_           =betas_for_alpha_bar(_lowerCAmelCase		,	alpha_transform_type="cosine"							)
         elif beta_schedule == "exp":
            UpperCAmelCase_           =betas_for_alpha_bar(_lowerCAmelCase		,	alpha_transform_type="exp"							)
         else:
            raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'							)

         UpperCAmelCase_           =1.0 - self.betas
         UpperCAmelCase_           =torch.cumprod(self.alphas		,	dim=0							)

         #  set all values
         self.set_timesteps(_lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =use_karras_sigmas



      def 						lowerCAmelCase__     (						self:    List[Any]		,	_lowerCAmelCase:    str		,	_lowerCAmelCase:    Any=None							)    ->     List[Any]:
         '''simple docstring'''

         if schedule_timesteps is None:
            UpperCAmelCase_           =self.timesteps

         UpperCAmelCase_           =(schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter							) == 0:
            UpperCAmelCase_           =1 if len(_lowerCAmelCase							) > 1 else 0
         else:
            UpperCAmelCase_           =timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase							) else timestep
            UpperCAmelCase_           =self._index_counter[timestep_int]

         return indices[pos].item()



      @property
      def 						lowerCAmelCase__     (						self:    int							)    ->     List[str]:
         '''simple docstring'''

         if self.config.timestep_spacing in ["linspace", "trailing"]:
            return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    Union[float, torch.FloatTensor]		,	)    ->     torch.FloatTensor:
         '''simple docstring'''

         UpperCAmelCase_           =self.index_for_timestep(_lowerCAmelCase							)

         UpperCAmelCase_           =self.sigmas[step_index]
         UpperCAmelCase_           =sample / ((sigma**2 + 1) ** 0.5)
         return sample



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    Union[str, torch.device] = None		,	_lowerCAmelCase:    Optional[int] = None		,	)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =num_inference_steps

         UpperCAmelCase_           =num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
            UpperCAmelCase_           =np.linspace(0		,	num_train_timesteps - 1		,	_lowerCAmelCase		,	dtype=_lowerCAmelCase							)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
            UpperCAmelCase_           =num_train_timesteps // self.num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            UpperCAmelCase_           =(np.arange(0		,	_lowerCAmelCase							) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase							)
            timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
            UpperCAmelCase_           =num_train_timesteps / self.num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            UpperCAmelCase_           =(np.arange(_lowerCAmelCase		,	0		,	-step_ratio							)).round().copy().astype(_lowerCAmelCase							)
            timesteps -= 1
         else:
            raise ValueError(
                F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'							)

         UpperCAmelCase_           =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
         UpperCAmelCase_           =np.log(_lowerCAmelCase							)
         UpperCAmelCase_           =np.interp(_lowerCAmelCase		,	np.arange(0		,	len(_lowerCAmelCase							)							)		,	_lowerCAmelCase							)

         if self.config.use_karras_sigmas:
            UpperCAmelCase_           =self._convert_to_karras(in_sigmas=_lowerCAmelCase		,	num_inference_steps=self.num_inference_steps							)
            UpperCAmelCase_           =np.array([self._sigma_to_t(_lowerCAmelCase		,	_lowerCAmelCase							) for sigma in sigmas]							)

         UpperCAmelCase_           =np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
         UpperCAmelCase_           =torch.from_numpy(_lowerCAmelCase							).to(device=_lowerCAmelCase							)
         UpperCAmelCase_           =torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2							), sigmas[-1:]]							)

         UpperCAmelCase_           =torch.from_numpy(_lowerCAmelCase							)
         UpperCAmelCase_           =torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2							)]							)

         if str(_lowerCAmelCase							).startswith("mps"							):
            # mps does not support float64
            UpperCAmelCase_           =timesteps.to(_lowerCAmelCase		,	dtype=torch.floataa							)
         else:
            UpperCAmelCase_           =timesteps.to(device=_lowerCAmelCase							)

         # empty dt and derivative
         UpperCAmelCase_           =None
         UpperCAmelCase_           =None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         UpperCAmelCase_           =defaultdict(_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	_lowerCAmelCase:    Optional[Any]		,	_lowerCAmelCase:    Any							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =np.log(_lowerCAmelCase							)

         # get distribution
         UpperCAmelCase_           =log_sigma - log_sigmas[:, np.newaxis]

         # get sigmas range
         UpperCAmelCase_           =np.cumsum((dists >= 0)		,	axis=0							).argmax(axis=0							).clip(max=log_sigmas.shape[0] - 2							)
         UpperCAmelCase_           =low_idx + 1

         UpperCAmelCase_           =log_sigmas[low_idx]
         UpperCAmelCase_           =log_sigmas[high_idx]

         # interpolate sigmas
         UpperCAmelCase_           =(low - log_sigma) / (low - high)
         UpperCAmelCase_           =np.clip(_lowerCAmelCase		,	0		,	1							)

         # transform interpolation to time range
         UpperCAmelCase_           =(1 - w) * low_idx + w * high_idx
         UpperCAmelCase_           =t.reshape(sigma.shape							)
         return t



      def 						lowerCAmelCase__     (						self:    int		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    Tuple							)    ->     torch.FloatTensor:
         '''simple docstring'''

         UpperCAmelCase_           =in_sigmas[-1].item()
         UpperCAmelCase_           =in_sigmas[0].item()

         UpperCAmelCase_           =7.0  # 7.0 is the value used in the paper
         UpperCAmelCase_           =np.linspace(0		,	1		,	_lowerCAmelCase							)
         UpperCAmelCase_           =sigma_min ** (1 / rho)
         UpperCAmelCase_           =sigma_max ** (1 / rho)
         UpperCAmelCase_           =(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas



      @property
      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     Optional[Any]:
         '''simple docstring'''

         return self.dt is None



      def 						lowerCAmelCase__     (						self:    int		,	_lowerCAmelCase:    Union[torch.FloatTensor, np.ndarray]		,	_lowerCAmelCase:    Union[float, torch.FloatTensor]		,	_lowerCAmelCase:    Union[torch.FloatTensor, np.ndarray]		,	_lowerCAmelCase:    bool = True		,	)    ->     Union[SchedulerOutput, Tuple]:
         '''simple docstring'''

         UpperCAmelCase_           =self.index_for_timestep(_lowerCAmelCase							)

         # advance index counter by 1
         UpperCAmelCase_           =timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase							) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
            UpperCAmelCase_           =self.sigmas[step_index]
            UpperCAmelCase_           =self.sigmas[step_index + 1]
         else:
            # 2nd order / Heun's method
            UpperCAmelCase_           =self.sigmas[step_index - 1]
            UpperCAmelCase_           =self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         UpperCAmelCase_           =0
         UpperCAmelCase_           =sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
            UpperCAmelCase_           =sigma_hat if self.state_in_first_order else sigma_next
            UpperCAmelCase_           =sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
            UpperCAmelCase_           =sigma_hat if self.state_in_first_order else sigma_next
            UpperCAmelCase_           =model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                sample / (sigma_input**2 + 1)
            )
         elif self.config.prediction_type == "sample":
            UpperCAmelCase_           =model_output
         else:
            raise ValueError(
                F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'							)

         if self.config.clip_sample:
            UpperCAmelCase_           =pred_original_sample.clamp(
                -self.config.clip_sample_range		,	self.config.clip_sample_range							)

         if self.state_in_first_order:
            # 2. Convert to an ODE derivative for 1st order
            UpperCAmelCase_           =(sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            UpperCAmelCase_           =sigma_next - sigma_hat

            # store for 2nd order step
            UpperCAmelCase_           =derivative
            UpperCAmelCase_           =dt
            UpperCAmelCase_           =sample
         else:
            # 2. 2nd order / Heun's method
            UpperCAmelCase_           =(sample - pred_original_sample) / sigma_next
            UpperCAmelCase_           =(self.prev_derivative + derivative) / 2

            # 3. take prev timestep & sample
            UpperCAmelCase_           =self.dt
            UpperCAmelCase_           =self.sample

            # free dt and derivative
            # Note, this puts the scheduler in "first order mode"
            UpperCAmelCase_           =None
            UpperCAmelCase_           =None
            UpperCAmelCase_           =None

         UpperCAmelCase_           =sample + derivative * dt

         if not return_dict:
            return (prev_sample,)

         return SchedulerOutput(prev_sample=_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    List[Any]		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    torch.FloatTensor		,	)    ->     torch.FloatTensor:
         '''simple docstring'''

         UpperCAmelCase_           =self.sigmas.to(device=original_samples.device		,	dtype=original_samples.dtype							)
         if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase							):
            # mps does not support float64
            UpperCAmelCase_           =self.timesteps.to(original_samples.device		,	dtype=torch.floataa							)
            UpperCAmelCase_           =timesteps.to(original_samples.device		,	dtype=torch.floataa							)
         else:
            UpperCAmelCase_           =self.timesteps.to(original_samples.device							)
            UpperCAmelCase_           =timesteps.to(original_samples.device							)

         UpperCAmelCase_           =[self.index_for_timestep(_lowerCAmelCase		,	_lowerCAmelCase							) for t in timesteps]

         UpperCAmelCase_           =sigmas[step_indices].flatten()
         while len(sigma.shape							) < len(original_samples.shape							):
            UpperCAmelCase_           =sigma.unsqueeze(-1							)

         UpperCAmelCase_           =original_samples + noise * sigma
         return noisy_samples






      def __len__(						self:    Dict							)    ->     Optional[Any]:
         '''simple docstring'''

         return self.config.num_train_timesteps





from abc import ABC, abstractmethod
from argparse import ArgumentParser





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		@staticmethod
		@abstractmethod
		def   _snake_case       (       snake_case__     :     ArgumentParser		)      ->	Tuple:
				raise NotImplementedError()



		@abstractmethod
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Optional[Any]:
				raise NotImplementedError()